Automated image registration of gated cardiac single‐photon emission computed tomography and magnetic resonance imaging
Bibliographic record
Abstract
PURPOSE: To develop an automatic registration method for electrocardiogram-gated myocardial perfusion single-photon emission computed tomography (SPECT) and cardiac cine-magnetic resonance imaging (MRI). MATERIALS AND METHODS: Paired myocardial perfusion SPECT (MPS) and MRI from 20 patients were considered. MR images were presegmented by heart localization based on detection of cardiac motion and optimal thresholding. A registration algorithm based on mutual information was subsequently applied to all time frames or a selected subset from both modalities. RESULTS: A preprocessing step significantly improved the accuracy of the registration when compared to automatic registration performed without preprocessing. Errors in translation parameters (T(x), T(y), T(z)) averaged (1.0 +/- 1.5, 1.1 +/- 1.3, 0.9 +/- 0.9) pixels with MRI segmentation and (4.6 +/- 3.2, 3.4 +/- 2.6, 3.0 +/- 3.4) pixels without MRI segmentation. Errors in rotation parameters (R(x), R(y), R(z)) averaged (5.4 +/- 2.9, 3.4 +/- 2.7, 4.5 +/- 3.6) degrees with MRI segmentation and (9.3 +/- 6.1, 4.8 +/- 4.3, 14.6 +/- 12.6) degrees without MRI segmentation. Error was calculated as the absolute difference between the expert manual and the automatic registration transformation. CONCLUSION: Automatic registration of gated MPS and cine MRI is possible with the use of a mutual information-based technique when MR images are presegmented. Cardiac motion can be used to isolate the left ventricle (LV) on the MR images automatically, and subsequently the segmented MR images can be coregistered with gated MPS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".